docstr: Forward function.
  
  Args:
      pred (Tensor): The prediction with shape (N, C), C is the
          number of classes.
      target (Tensor): The learning target of the iou-aware
          classification score with shape (N, C), C is
          the number of classes.
      weight (Tensor, optional): The weight of loss for each
          prediction. Defaults to None.
      avg_factor (int, optional): Average factor that is used to average
          the loss. Defaults to None.
      reduction_override (str, optional): The reduction method used to
          override the original reduction method of the loss.
          Options are "none", "mean" and "sum".
  
  Returns:
      Tensor: The calculated loss
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code: def forward(
      self,
      pred: Tensor,
      target: Tensor,
      weight: Optional[Tensor] = None,
      avg_factor: Optional[int] = None,
      reduction_override: Optional[str] = None,
  ) -> Tensor:
      """Forward function.
  
      Args:
          pred (Tensor): The prediction with shape (N, C), C is the
              number of classes.
          target (Tensor): The learning target of the iou-aware
              classification score with shape (N, C), C is
              the number of classes.
          weight (Tensor, optional): The weight of loss for each
              prediction. Defaults to None.
          avg_factor (int, optional): Average factor that is used to average
              the loss. Defaults to None.
          reduction_override (str, optional): The reduction method used to
              override the original reduction method of the loss.
              Options are "none", "mean" and "sum".
  
      Returns:
          Tensor: The calculated loss
      """
      assert reduction_override in (None, "none", "mean", "sum")
      reduction = (
          reduction_override if reduction_override else self.reduction
      )
      if self.use_sigmoid:
          loss_cls = self.loss_weight * varifocal_loss(
              pred,
              target,
              weight,
              alpha=self.alpha,
              gamma=self.gamma,
              iou_weighted=self.iou_weighted,
              reduction=reduction,
              avg_factor=avg_factor,
          )
      else:
          raise NotImplementedError
      return loss_cls
  
file_path: mmdetection/mmdet/models/losses/varifocal_loss.py
imports: from typing import Optional
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from mmdet.registry import MODELS
  from .utils import weight_reduce_loss
label_desc: Use image node_id 2 for calling the VarifocalLoss obj's underlying member method code with example usage: obj.forward(pred, target, weight, avg_factor, reduction_override) and returns: loss_cls